Instructions to use sulaimank/w2vbert-lingala-sd3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sulaimank/w2vbert-lingala-sd3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="sulaimank/w2vbert-lingala-sd3")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("sulaimank/w2vbert-lingala-sd3") model = AutoModelForCTC.from_pretrained("sulaimank/w2vbert-lingala-sd3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ae7c1113d4153b88a4c2751db35feb2cd976abffc3808a67cf4dbdaf0249e9ea
- Size of remote file:
- 5.97 kB
- SHA256:
- e736aaad8c04cd919f820844d246b25a18cd45e8cd7badae6a93d71ddd1ee78e
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